汇总数据

rm(list = ls())
load("All.RData")
all_block <- all
load("../AmygdalaTENT/Alltent.RData")

# 分组统计
analyze <- describeBy(all_block[2:14],list(all_block$position,all_block$condition),mat = TRUE)
# 把vars列中的数字替换为名称
varnames <- row.names(analyze)
tempstr <- strsplit(varnames,"[0-9]")
varnames <- sapply(tempstr,'[',1)
analyze$vars <- as.factor(varnames)

# TENT的数据
analyze_tent <- describeBy(all[2:12],list(all$position,all$condition,all$stimuli),mat = TRUE)
# 把vars列中的数字替换为TR名称
varnames <- row.names(analyze_tent)
varnames <- substr(varnames,1,4)
analyze_tent$vars <- as.factor(varnames)

# 备份 把是odor还是face作为一个变量
# library(psych)
# analyze <- describeBy(all_block[2:14],list(all_block$valance,all_block$position,all_block$condition),mat = TRUE)
# analyze <- na.omit(analyze)
# varnames <- row.names(analyze)
# tempstr <- strsplit(varnames,"[0-9]")
# varnames <- sapply(tempstr,'[',1)
# analyze$vars <- as.factor(varnames)
# 提取需要画图的部分
# datachosen <- subset(analyze,group1=="Face" & group2=="Amy" & vars%in%c('FP','FU','HP','HU'),select = c(mean,se,vars,group3))

分析

统计voxel的数量

# 画图时的字体大小
WORD_SIZE = 15

for (val in unique(all_block$valance)) {
voxel <- subset(all_block,valance==val & position%in%c("medialAmy","lateralAmy"),
                select = c(Sub,Count,position,condition))
#long format data
MANOVA(voxel,subID = 'Sub',dv='Count',within = c("position","condition"))

# 绘图
ggthemr('fresh',layout = "clean")
# face或者odor对应的条件
condition <- unique(subset(all_block,valance==val)$condition)
# 图的标题
title <- val
# 提取需要画图的部分
datachosen <- subset(analyze,group1%in%c("medialAmy","lateralAmy") 
                     & group2%in%condition 
                     & vars=='Count',select = c(mean,se,vars,group1,group2))
# Error bars represent standard error of the mean
figure <- ggplot(datachosen, aes(x=group1, y=mean, fill=group2)) + 
  #coord_cartesian(ylim=c(0,0.4)) +  # 设置y轴坐标范围
  labs(title = title ,x='Position',y='Voxel Count',fill='Preference')+#设置坐标轴
  theme(axis.text.x = element_text(size=WORD_SIZE),  # 设置x轴字体大小,以下同理
        axis.text.y = element_text(size=WORD_SIZE), 
        axis.title.x = element_text(size=WORD_SIZE), 
        axis.title.y = element_text(size=WORD_SIZE),
        legend.title = element_text(size=WORD_SIZE),
        legend.text = element_text(size=WORD_SIZE),
        plot.title = element_text(hjust = 0.5)) + 
  scale_y_continuous(breaks=waiver(),expand = c(0,0))+
  geom_bar(position="dodge", stat="identity") +
  # scale_fill_manual(values = colors[1:2])+ #颜色
  scale_fill_brewer(palette = "Set2",direction = -1)+ #颜色
  geom_errorbar(aes(ymin=mean-se, ymax=mean+se),
                width=.2,color='black',      # Width of the error bars
                position=position_dodge(.9))
print(figure)


}
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ──────────────────────────────────────
##    position condition   Mean   S.D.  N
## ──────────────────────────────────────
##  lateralAmy        FH 138.95 130.51 20
##  lateralAmy        HF 100.20  88.87 20
##  medialAmy         FH 236.15 189.44 20
##  medialAmy         HF 182.60 166.20 20
## ──────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      Count
## Between-subjects factor(s): -
## Within-subjects factor(s):  position, condition
## Covariate(s):               -
## ─────────────────────────────────────────────────────────────────────────────────────
##                             MS       MSE df1 df2     F     p      η²p   [90%     CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## position            161280.800  3856.668   1  19 41.82 <.001 *** 0.688 [0.436, 0.786]
## condition            42596.450 62023.739   1  19  0.69  .418     0.035 [0.000, 0.225]
## position:condition    1095.200  9828.279   1  19  0.11  .742     0.006 [0.000, 0.140]
## ─────────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                         ω²      η²   η²[G]   η²[p] Cohen's f
## position             0.080   0.086   0.088   0.688     1.485
## condition            0.017   0.023   0.025   0.035     0.190
## position:condition  -0.005   0.001   0.001   0.006     0.078
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:

## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ──────────────────────────────────────
##    position condition   Mean   S.D.  N
## ──────────────────────────────────────
##  lateralAmy        PU 140.15 107.71 20
##  lateralAmy        UP 100.65  97.39 20
##  medialAmy         PU 405.15 339.96 20
##  medialAmy         UP 142.80 132.62 20
## ──────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      Count
## Between-subjects factor(s): -
## Within-subjects factor(s):  position, condition
## Covariate(s):               -
## ─────────────────────────────────────────────────────────────────────────────────────
##                             MS       MSE df1 df2     F     p      η²p   [90%     CI]
## ─────────────────────────────────────────────────────────────────────────────────────
## position            471705.612 15655.244   1  19 30.13 <.001 *** 0.613 [0.333, 0.735]
## condition           455567.113 85851.007   1  19  5.31  .033 *   0.218 [0.010, 0.436]
## position:condition  248310.612 22122.086   1  19 11.22  .003 **  0.371 [0.090, 0.561]
## ─────────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                         ω²      η²   η²[G]   η²[p] Cohen's f
## position             0.109   0.115   0.139   0.613     1.259
## condition            0.105   0.111   0.135   0.218     0.528
## position:condition   0.055   0.060   0.078   0.371     0.768
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:

方差分析和画图

# 颜色
gf_color <- c("#A1D99B","#41AB5D","#9ECAE1","#4292C6","#FDAE6B","#F16913","#BCBDDC","#807DBA")
# 绘图
ggthemr('fresh',layout = "clean")
# layout(matrix(c(1,1,2,3),2,2,byrow=T))

# 4个选取ROI的方式,这里是先选出是face还是odor
for (val in unique(all_block$valance)) {
# 3个杏仁核的位置
  for (pos in unique(all_block$position)) {
    
    cat("\n作差并取绝对值之后\n")
    # 进行方差分析
    # 选择对应的数据
    data_anova <- subset(all_block,position==pos & valance==val,select = c(1:14,16))
    data_anova$condition <- paste0('condition_',data_anova$condition) 
    #更改变量名称
    name <- paste(rep(c('Face_F','Face_H'),each=4),
                  rep(c('Odor_P','Odor_U'),each=2,times=2),
                  rep(c('Visi_I','Visi_V'),times=4),
                  sep = '_')
    name2 <- paste(rep(c('Face_F','Face_H'),each=2),rep(c('Odor_P','Odor_U'),times=2),sep = '_')
    #重命名
    names(data_anova) <- c('Sub',name,'Count',name2,'condition')
    
    
    
    
    # 处理没有做差的部分
    data_anova4 <- data_anova[c(1,11:15)]
    #更换数据格式
    data_anova4 <- melt(data_anova4,c('Sub','condition'))
    data_anova4 <- dcast(data_anova4,Sub~condition+variable)
    
    #方差分析
    MANOVA(data_anova4,dvs =names(data_anova4[-1]),
           dvs.pattern ='condition_(..)_Face_(.)_Odor_(.)',
           within=c('Condition','Face','Odor'))    
    # %>%
    # EMMEANS("Odor", by="Face")
    # 简单效应
    # MANOVA(data_anova4[seq(2,9,2)],dvs =names(data_anova4[seq(2,9,2)]),
    #        dvs.pattern ='condition_(..)_Face_(.)',
    #        within=c('Condition','Face')) 
    # 
    
    
    
    cat("\n全部8个条件\n")
    # 进行方差分析
    # 选择对应的数据没有做差的部分
    print(paste0(pos, val))
    data_anova8 <- data_anova[c(1:9,15)]
    #更换数据格式
    data_anova8 <- melt(data_anova8,c('Sub','condition'))
    data_anova8 <- dcast(data_anova8,Sub~condition+variable)
    
    #方差分析
    MANOVA(data_anova8,dvs =names(data_anova8[-1]),
           dvs.pattern ='condition_(..)_Face_(.)_Odor_(.)_Visi_(.)',
           within=c('Condition','Face','Odor','Visi'))    
    
    
    
    
    cat("\n绘图\n")
    
    # face或者odor对应的条件
    condition <- unique(subset(all_block,valance==val)$condition)
    # 图的标题
    title <- paste0(pos, val)
    # 提取需要画图的部分
    datachosen <- subset(analyze,group1==pos & group2%in%condition 
                         & vars%in%c('FP','FU','HP','HU'),select = c(mean,se,vars,group2))
    # Error bars represent standard error of the mean
    figure_4 <- ggplot(datachosen, aes(x=vars, y=mean, fill=group2)) + 
      #coord_cartesian(ylim=c(0,0.4)) +  # 设置y轴坐标范围
      labs(title = title ,x='Condition',y='Mean β',fill='ROI')+#设置坐标轴
      theme(axis.text.x = element_text(size=WORD_SIZE),  # 设置x轴字体大小,以下同理
            axis.text.y = element_text(size=WORD_SIZE), 
            axis.title.x = element_text(size=WORD_SIZE), 
            axis.title.y = element_text(size=WORD_SIZE),
            legend.title = element_text(size=WORD_SIZE),
            legend.text = element_text(size=WORD_SIZE),
            plot.title = element_text(hjust = 0.5)) + 
      scale_y_continuous(expand = c(0,0))+
      geom_bar(position="dodge", stat="identity") +
      # scale_fill_manual(values = colors[1:2])+ #颜色
      scale_fill_brewer(palette = "Set2",direction = -1)+ #颜色
      geom_errorbar(aes(ymin=mean-se, ymax=mean+se),
                    width=.2,color='black',      # Width of the error bars
                    position=position_dodge(.9))
    
    
    
    # 8个条件没有作差的
    datachosen <- subset(analyze,group1==pos & group2%in%condition 
                         & vars%in%c('FPI','FPV','FUI','FUV','HPI','HPV','HUI','HUV'),
                         select = c(mean,se,vars,group2))
    # Error bars represent standard error of the mean
    figure_8 <- ggplot(datachosen, aes(x=vars, y=mean, fill=group2)) + 
      coord_cartesian(ylim=c(-0.5,0.5)) +  # 设置y轴坐标范围
      labs(title = title ,x='Condition',y='Mean β',fill='ROI')+#设置坐标轴
      theme(axis.text.x = element_text(size=WORD_SIZE),  # 设置x轴字体大小,以下同理
            axis.text.y = element_text(size=WORD_SIZE), 
            axis.title.x = element_text(size=WORD_SIZE), 
            axis.title.y = element_text(size=WORD_SIZE),
            legend.title = element_text(size=WORD_SIZE),
            legend.text = element_text(size=WORD_SIZE),
            plot.title = element_text(hjust = 0.5)) + 
      scale_y_continuous(expand = c(0,0))+
      geom_bar(position="dodge", stat="identity") +
      # scale_fill_manual(values = colors[1:2])+ #颜色
      scale_fill_brewer(palette = "Set2",direction = -1)+ #颜色
      geom_errorbar(aes(ymin=mean-se, ymax=mean+se),
                    width=.2,color='black',      # Width of the error bars
                    position=position_dodge(.9))
      
    # TENT
    # 选择数据
    # datachosen <- subset(all,position==pos & valance==val,select = c(1:12,14,16))
    # 作差之后的结果
    pd <- position_dodge(0.9)
    datachosen <- subset(analyze_tent,group1==pos & group2%in%condition
                         & group3%in%c('FP','FU','HP','HU'),
                         select = c(mean,se,vars,group2,group3))
    figtent_4 <- ggplot(datachosen, aes(x=vars, y=mean,
                           group=interaction(group2,group3),color=group3)) + 
      labs(x='TR',y='Mean β',color='Condition')+#设置坐标轴,linetype='ROI'
      # scale_color_brewer(palette = "Set2",direction = -1)+ #颜色
      scale_color_manual(values = gf_color[seq(2,8,2)])+ #自选颜色
      # scale_linetype_manual(values=c("solid", "longdash"))+
      facet_wrap(~group2,ncol = 1,scales="free")+#分面 +facet_wrap(~cyl,ncol-1,scales="free")
      theme(axis.text.x = element_text(size=WORD_SIZE),  # 设置x轴字体大小,以下同理
            axis.text.y = element_text(size=WORD_SIZE), 
            axis.title.x = element_text(size=WORD_SIZE), 
            axis.title.y = element_text(size=WORD_SIZE),
            legend.title = element_text(size=WORD_SIZE),
            legend.text = element_text(size=WORD_SIZE),
            text = element_text(size=WORD_SIZE),
            plot.title = element_text(hjust = 0.5))+ 
      scale_x_discrete(labels=as.character(0:10),expand = c(0,0))+
      geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.1,position = pd) +
      geom_line(position = pd) +
      geom_point(position = pd)
    # 没有作差的结果
    datachosen <- subset(analyze_tent,group1==pos & group2%in%condition 
                         & group3%in%c('FPI','FPV','FUI','FUV','HPI','HPV','HUI','HUV'),
                         select = c(mean,se,vars,group2,group3))
    
    figtent_8 <- ggplot(datachosen, aes(x=vars, y=mean,
                                        group=interaction(group2,group3),color=group3)) + 
      labs(x='TR',y='Mean β',color='Condition')+#设置坐标轴,linetype='ROI'
      # scale_color_brewer(palette = "Set2",direction = -1)+ #颜色
      scale_color_manual(values = gf_color)+ #自选颜色
      # scale_linetype_manual(values=c("solid", "longdash"))+
      facet_wrap(~group2,ncol = 1,scales="free")+#分面 +facet_wrap(~cyl,ncol-1,scales="free")
      theme(axis.text.x = element_text(size=WORD_SIZE),  # 设置x轴字体大小,以下同理
            axis.text.y = element_text(size=WORD_SIZE), 
            axis.title.x = element_text(size=WORD_SIZE), 
            axis.title.y = element_text(size=WORD_SIZE),
            legend.title = element_text(size=WORD_SIZE),
            legend.text = element_text(size=WORD_SIZE),
            text = element_text(size=WORD_SIZE),
            plot.title = element_text(hjust = 0.5))+ 
      scale_x_discrete(labels=as.character(0:10),expand = c(0,0))+
      geom_errorbar(aes(ymin=mean-se, ymax=mean+se), width=.1,position = pd) +
      geom_line(position = pd) +
      geom_point(position = pd)
    
    # 合并几个图到一个里面
    # ggarrange(p2,ggarrange(p1,p3,ncol=2,labels=c("B","C")),nrow=2,labels="A")
    block <- ggarrange(figure_4,figure_8,ncol = 2,labels=c("A","B"),widths = c(3,4))
    tent <- ggarrange(figtent_4,figtent_8,ncol = 2,labels=c("C","D"))
    print(ggarrange(block,tent,nrow = 2,heights = c(1,3)))
    # 测试保存图片
    # jpeg(file="myplot.jpeg",width = 1920,height = 1080)
    # ggarrange(figure_4,figure_8,ncol = 2,labels=c("A","B"),widths = c(1,2))
    # dev.off()
  }
}
## 
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ─────────────────────────────────
##  Condition Face Odor Mean S.D.  N
## ─────────────────────────────────
##         FH    F    P 0.25 0.13 20
##         FH    F    U 0.30 0.16 20
##         FH    H    P 0.22 0.12 20
##         FH    H    U 0.15 0.09 20
##         HF    F    P 0.15 0.08 20
##         HF    F    U 0.30 0.23 20
##         HF    H    P 0.33 0.20 20
##         HF    H    U 0.31 0.27 20
## ─────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_FH_Face_F_Odor_P, condition_FH_Face_F_Odor_U, condition_FH_Face_H_Odor_P, condition_FH_Face_H_Odor_U, condition_HF_Face_F_Odor_P, condition_HF_Face_F_Odor_U, condition_HF_Face_H_Odor_P, condition_HF_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor
## Covariate(s):               -
## ─────────────────────────────────────────────────────────────────────────────
##                         MS   MSE df1 df2     F     p      η²p   [90%     CI]
## ─────────────────────────────────────────────────────────────────────────────
## Condition            0.088 0.032   1  19  2.79  .111     0.128 [0.000, 0.349]
## Face                 0.001 0.030   1  19  0.02  .876     0.001 [0.000, 0.080]
## Odor                 0.029 0.007   1  19  3.93  .062 .   0.171 [0.000, 0.393]
## Condition:Face       0.354 0.037   1  19  9.68  .006 **  0.337 [0.068, 0.535]
## Condition:Odor       0.056 0.036   1  19  1.55  .229     0.075 [0.000, 0.287]
## Face:Odor            0.220 0.021   1  19 10.31  .005 **  0.352 [0.077, 0.546]
## Condition:Face:Odor  0.004 0.033   1  19  0.13  .721     0.007 [0.000, 0.146]
## ─────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                          ω²      η²   η²[G]   η²[p] Cohen's f
## Condition             0.010   0.017   0.019   0.128     0.383
## Face                 -0.006   0.000   0.000   0.001     0.032
## Odor                 -0.001   0.005   0.006   0.171     0.454
## Condition:Face        0.060   0.066   0.072   0.337     0.713
## Condition:Odor        0.004   0.011   0.012   0.075     0.285
## Face:Odor             0.035   0.041   0.046   0.352     0.737
## Condition:Face:Odor  -0.005   0.001   0.001   0.007     0.084
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 全部8个条件
## [1] "AmyFace"
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ───────────────────────────────────────
##  Condition Face Odor Visi  Mean S.D.  N
## ───────────────────────────────────────
##         FH    F    P    I  0.02 0.18 20
##         FH    F    P    V  0.26 0.18 20
##         FH    F    U    I -0.13 0.15 20
##         FH    F    U    V  0.17 0.15 20
##         FH    H    P    I -0.07 0.13 20
##         FH    H    P    V -0.27 0.17 20
##         FH    H    U    I -0.09 0.13 20
##         FH    H    U    V -0.20 0.15 20
##         HF    F    P    I -0.11 0.20 20
##         HF    F    P    V -0.20 0.15 20
##         HF    F    U    I -0.08 0.19 20
##         HF    F    U    V -0.36 0.23 20
##         HF    H    P    I -0.06 0.16 20
##         HF    H    P    V  0.27 0.17 20
##         HF    H    U    I -0.11 0.20 20
##         HF    H    U    V  0.19 0.23 20
## ───────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_FH_Face_F_Odor_P_Visi_I, condition_FH_Face_F_Odor_P_Visi_V, condition_FH_Face_F_Odor_U_Visi_I, condition_FH_Face_F_Odor_U_Visi_V, condition_FH_Face_H_Odor_P_Visi_I, condition_FH_Face_H_Odor_P_Visi_V, condition_FH_Face_H_Odor_U_Visi_I, condition_FH_Face_H_Odor_U_Visi_V, condition_HF_Face_F_Odor_P_Visi_I, condition_HF_Face_F_Odor_P_Visi_V, condition_HF_Face_F_Odor_U_Visi_I, condition_HF_Face_F_Odor_U_Visi_V, condition_HF_Face_H_Odor_P_Visi_I, condition_HF_Face_H_Odor_P_Visi_V, condition_HF_Face_H_Odor_U_Visi_I, condition_HF_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor, Visi
## Covariate(s):               -
## ───────────────────────────────────────────────────────────────────────────────────
##                              MS   MSE df1 df2      F     p      η²p   [90%     CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition                 0.029 0.072   1  19   0.39  .537     0.020 [0.000, 0.195]
## Face                      0.014 0.020   1  19   0.67  .424     0.034 [0.000, 0.224]
## Odor                      0.256 0.055   1  19   4.62  .045 *   0.196 [0.003, 0.416]
## Visi                      0.300 0.030   1  19  10.05  .005 **  0.346 [0.073, 0.542]
## Condition:Face            5.036 0.012   1  19 431.00 <.001 *** 0.958 [0.914, 0.971]
## Condition:Odor            0.003 0.027   1  19   0.13  .727     0.007 [0.000, 0.144]
## Face:Odor                 0.106 0.017   1  19   6.26  .022 *   0.248 [0.022, 0.462]
## Condition:Visi            0.002 0.011   1  19   0.14  .708     0.008 [0.000, 0.150]
## Face:Visi                 0.036 0.017   1  19   2.08  .166     0.099 [0.000, 0.316]
## Odor:Visi                 0.005 0.027   1  19   0.20  .661     0.010 [0.000, 0.163]
## Condition:Face:Odor       0.097 0.010   1  19   9.97  .005 **  0.344 [0.072, 0.540]
## Condition:Face:Visi       4.288 0.015   1  19 284.33 <.001 *** 0.937 [0.873, 0.957]
## Condition:Odor:Visi       0.181 0.017   1  19  10.42  .004 **  0.354 [0.078, 0.548]
## Face:Odor:Visi            0.042 0.026   1  19   1.58  .224     0.077 [0.000, 0.289]
## Condition:Face:Odor:Visi  0.022 0.007   1  19   3.09  .095 .   0.140 [0.000, 0.361]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                               ω²      η²   η²[G]   η²[p] Cohen's f
## Condition                  0.001   0.001   0.003   0.020     0.143
## Face                       0.000   0.001   0.001   0.034     0.188
## Odor                       0.013   0.013   0.026   0.196     0.494
## Visi                       0.015   0.015   0.031   0.346     0.727
## Condition:Face             0.253   0.253   0.347   0.958     4.776
## Condition:Odor             0.000   0.000   0.000   0.007     0.084
## Face:Odor                  0.005   0.005   0.011   0.248     0.574
## Condition:Visi             0.000   0.000   0.000   0.008     0.090
## Face:Visi                  0.001   0.002   0.004   0.099     0.331
## Odor:Visi                  0.000   0.000   0.001   0.010     0.101
## Condition:Face:Odor        0.005   0.005   0.010   0.344     0.724
## Condition:Face:Visi        0.215   0.216   0.312   0.937     3.857
## Condition:Odor:Visi        0.009   0.009   0.019   0.354     0.740
## Face:Odor:Visi             0.002   0.002   0.004   0.077     0.289
## Condition:Face:Odor:Visi   0.001   0.001   0.002   0.140     0.403
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 绘图

## 
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ─────────────────────────────────
##  Condition Face Odor Mean S.D.  N
## ─────────────────────────────────
##         FH    F    P 0.18 0.11 20
##         FH    F    U 0.25 0.13 20
##         FH    H    P 0.20 0.13 20
##         FH    H    U 0.14 0.09 20
##         HF    F    P 0.13 0.09 20
##         HF    F    U 0.18 0.13 20
##         HF    H    P 0.25 0.19 20
##         HF    H    U 0.24 0.15 20
## ─────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_FH_Face_F_Odor_P, condition_FH_Face_F_Odor_U, condition_FH_Face_H_Odor_P, condition_FH_Face_H_Odor_U, condition_HF_Face_F_Odor_P, condition_HF_Face_F_Odor_U, condition_HF_Face_H_Odor_P, condition_HF_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor
## Covariate(s):               -
## ────────────────────────────────────────────────────────────────────────────
##                         MS   MSE df1 df2    F     p      η²p   [90%     CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition            0.001 0.008   1  19 0.17  .689     0.009 [0.000, 0.156]
## Face                 0.024 0.018   1  19 1.32  .264     0.065 [0.000, 0.273]
## Odor                 0.007 0.013   1  19 0.53  .475     0.027 [0.000, 0.210]
## Condition:Face       0.165 0.020   1  19 8.48  .009 **  0.309 [0.051, 0.512]
## Condition:Odor       0.001 0.024   1  19 0.04  .847     0.002 [0.000, 0.097]
## Face:Odor            0.109 0.016   1  19 6.77  .018 *   0.263 [0.028, 0.475]
## Condition:Face:Odor  0.010 0.023   1  19 0.45  .510     0.023 [0.000, 0.201]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                          ω²      η²   η²[G]   η²[p] Cohen's f
## Condition            -0.007   0.000   0.000   0.009     0.095
## Face                  0.000   0.008   0.009   0.065     0.264
## Odor                 -0.006   0.002   0.003   0.027     0.167
## Condition:Face        0.049   0.057   0.060   0.309     0.669
## Condition:Odor       -0.008   0.000   0.000   0.002     0.045
## Face:Odor             0.030   0.038   0.041   0.263     0.597
## Condition:Face:Odor  -0.004   0.004   0.004   0.023     0.153
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 全部8个条件
## [1] "lateralAmyFace"
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ───────────────────────────────────────
##  Condition Face Odor Visi  Mean S.D.  N
## ───────────────────────────────────────
##         FH    F    P    I  0.00 0.15 20
##         FH    F    P    V  0.17 0.10 20
##         FH    F    U    I -0.11 0.13 20
##         FH    F    U    V  0.14 0.12 20
##         FH    H    P    I -0.07 0.12 20
##         FH    H    P    V -0.25 0.16 20
##         FH    H    U    I -0.08 0.14 20
##         FH    H    U    V -0.17 0.11 20
##         HF    F    P    I -0.11 0.18 20
##         HF    F    P    V -0.18 0.16 20
##         HF    F    U    I -0.08 0.12 20
##         HF    F    U    V -0.24 0.14 20
##         HF    H    P    I -0.07 0.11 20
##         HF    H    P    V  0.18 0.17 20
##         HF    H    U    I -0.05 0.15 20
##         HF    H    U    V  0.18 0.14 20
## ───────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_FH_Face_F_Odor_P_Visi_I, condition_FH_Face_F_Odor_P_Visi_V, condition_FH_Face_F_Odor_U_Visi_I, condition_FH_Face_F_Odor_U_Visi_V, condition_FH_Face_H_Odor_P_Visi_I, condition_FH_Face_H_Odor_P_Visi_V, condition_FH_Face_H_Odor_U_Visi_I, condition_FH_Face_H_Odor_U_Visi_V, condition_HF_Face_F_Odor_P_Visi_I, condition_HF_Face_F_Odor_P_Visi_V, condition_HF_Face_F_Odor_U_Visi_I, condition_HF_Face_F_Odor_U_Visi_V, condition_HF_Face_H_Odor_P_Visi_I, condition_HF_Face_H_Odor_P_Visi_V, condition_HF_Face_H_Odor_U_Visi_I, condition_HF_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor, Visi
## Covariate(s):               -
## ───────────────────────────────────────────────────────────────────────────────────
##                              MS   MSE df1 df2      F     p      η²p   [90%     CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition                 0.000 0.070   1  19   0.00  .979     0.000 [0.000, 1.000]
## Face                      0.008 0.006   1  19   1.36  .258     0.067 [0.000, 0.275]
## Odor                      0.010 0.014   1  19   0.71  .410     0.036 [0.000, 0.228]
## Visi                      0.196 0.014   1  19  13.68  .002 **  0.419 [0.126, 0.597]
## Condition:Face            3.205 0.010   1  19 325.74 <.001 *** 0.945 [0.888, 0.962]
## Condition:Odor            0.003 0.011   1  19   0.29  .598     0.015 [0.000, 0.180]
## Face:Odor                 0.094 0.007   1  19  13.09  .002 **  0.408 [0.118, 0.589]
## Condition:Visi            0.011 0.010   1  19   1.13  .301     0.056 [0.000, 0.260]
## Face:Visi                 0.000 0.005   1  19   0.05  .829     0.003 [0.000, 0.107]
## Odor:Visi                 0.006 0.018   1  19   0.32  .581     0.016 [0.000, 0.184]
## Condition:Face:Odor       0.039 0.008   1  19   4.76  .042 *   0.200 [0.004, 0.420]
## Condition:Face:Visi       2.499 0.010   1  19 242.28 <.001 *** 0.927 [0.853, 0.950]
## Condition:Odor:Visi       0.098 0.013   1  19   7.40  .014 *   0.280 [0.036, 0.489]
## Face:Odor:Visi            0.012 0.016   1  19   0.77  .392     0.039 [0.000, 0.232]
## Condition:Face:Odor:Visi  0.007 0.006   1  19   1.20  .287     0.059 [0.000, 0.265]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                               ω²      η²   η²[G]   η²[p] Cohen's f
## Condition                  0.000   0.000   0.000   0.000     0.000
## Face                       0.000   0.001   0.001   0.067     0.268
## Odor                       0.000   0.001   0.002   0.036     0.193
## Visi                       0.016   0.016   0.032   0.419     0.849
## Condition:Face             0.266   0.266   0.354   0.945     4.145
## Condition:Odor             0.000   0.000   0.001   0.015     0.123
## Face:Odor                  0.007   0.008   0.016   0.408     0.830
## Condition:Visi             0.000   0.001   0.002   0.056     0.244
## Face:Visi                  0.000   0.000   0.000   0.003     0.055
## Odor:Visi                  0.000   0.000   0.001   0.016     0.128
## Condition:Face:Odor        0.003   0.003   0.007   0.200     0.500
## Condition:Face:Visi        0.207   0.208   0.299   0.927     3.564
## Condition:Odor:Visi        0.008   0.008   0.016   0.280     0.624
## Face:Odor:Visi             0.001   0.001   0.002   0.039     0.201
## Condition:Face:Odor:Visi   0.000   0.001   0.001   0.059     0.250
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 绘图

## 
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ─────────────────────────────────
##  Condition Face Odor Mean S.D.  N
## ─────────────────────────────────
##         FH    F    P 0.29 0.16 20
##         FH    F    U 0.31 0.19 20
##         FH    H    P 0.23 0.14 20
##         FH    H    U 0.17 0.11 20
##         HF    F    P 0.21 0.10 20
##         HF    F    U 0.37 0.30 20
##         HF    H    P 0.40 0.25 20
##         HF    H    U 0.37 0.41 20
## ─────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_FH_Face_F_Odor_P, condition_FH_Face_F_Odor_U, condition_FH_Face_H_Odor_P, condition_FH_Face_H_Odor_U, condition_HF_Face_F_Odor_P, condition_HF_Face_F_Odor_U, condition_HF_Face_H_Odor_P, condition_HF_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor
## Covariate(s):               -
## ────────────────────────────────────────────────────────────────────────────
##                         MS   MSE df1 df2    F     p      η²p   [90%     CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition            0.282 0.079   1  19 3.59  .073 .   0.159 [0.000, 0.381]
## Face                 0.000 0.052   1  19 0.00  .982     0.000 [0.000, 1.000]
## Odor                 0.022 0.015   1  19 1.47  .240     0.072 [0.000, 0.282]
## Condition:Face       0.396 0.049   1  19 8.04  .011 *   0.297 [0.045, 0.503]
## Condition:Odor       0.070 0.051   1  19 1.38  .255     0.068 [0.000, 0.276]
## Face:Odor            0.185 0.028   1  19 6.56  .019 *   0.257 [0.025, 0.469]
## Condition:Face:Odor  0.034 0.040   1  19 0.85  .369     0.043 [0.000, 0.239]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                          ω²      η²   η²[G]   η²[p] Cohen's f
## Condition             0.027   0.031   0.034   0.159     0.435
## Face                 -0.004   0.000   0.000   0.000     0.000
## Odor                 -0.002   0.002   0.003   0.072     0.279
## Condition:Face        0.039   0.044   0.047   0.297     0.650
## Condition:Odor        0.003   0.008   0.009   0.068     0.270
## Face:Odor             0.016   0.021   0.023   0.257     0.588
## Condition:Face:Odor  -0.001   0.004   0.004   0.043     0.212
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 全部8个条件
## [1] "medialAmyFace"
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ───────────────────────────────────────
##  Condition Face Odor Visi  Mean S.D.  N
## ───────────────────────────────────────
##         FH    F    P    I  0.02 0.20 20
##         FH    F    P    V  0.30 0.21 20
##         FH    F    U    I -0.13 0.18 20
##         FH    F    U    V  0.18 0.18 20
##         FH    H    P    I -0.07 0.14 20
##         FH    H    P    V -0.28 0.19 20
##         FH    H    U    I -0.09 0.15 20
##         FH    H    U    V -0.20 0.19 20
##         HF    F    P    I -0.07 0.30 20
##         HF    F    P    V -0.19 0.20 20
##         HF    F    U    I -0.06 0.30 20
##         HF    F    U    V -0.40 0.27 20
##         HF    H    P    I -0.05 0.20 20
##         HF    H    P    V  0.34 0.23 20
##         HF    H    U    I -0.11 0.31 20
##         HF    H    U    V  0.22 0.30 20
## ───────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_FH_Face_F_Odor_P_Visi_I, condition_FH_Face_F_Odor_P_Visi_V, condition_FH_Face_F_Odor_U_Visi_I, condition_FH_Face_F_Odor_U_Visi_V, condition_FH_Face_H_Odor_P_Visi_I, condition_FH_Face_H_Odor_P_Visi_V, condition_FH_Face_H_Odor_U_Visi_I, condition_FH_Face_H_Odor_U_Visi_V, condition_HF_Face_F_Odor_P_Visi_I, condition_HF_Face_F_Odor_P_Visi_V, condition_HF_Face_F_Odor_U_Visi_I, condition_HF_Face_F_Odor_U_Visi_V, condition_HF_Face_H_Odor_P_Visi_I, condition_HF_Face_H_Odor_P_Visi_V, condition_HF_Face_H_Odor_U_Visi_I, condition_HF_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor, Visi
## Covariate(s):               -
## ───────────────────────────────────────────────────────────────────────────────────
##                              MS   MSE df1 df2      F     p      η²p   [90%     CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition                 0.004 0.130   1  19   0.03  .863     0.002 [0.000, 0.089]
## Face                      0.011 0.049   1  19   0.23  .640     0.012 [0.000, 0.169]
## Odor                      0.446 0.071   1  19   6.23  .022 *   0.247 [0.021, 0.461]
## Visi                      0.356 0.046   1  19   7.66  .012 *   0.287 [0.040, 0.495]
## Condition:Face            5.661 0.026   1  19 214.78 <.001 *** 0.919 [0.836, 0.944]
## Condition:Odor            0.027 0.037   1  19   0.75  .398     0.038 [0.000, 0.231]
## Face:Odor                 0.134 0.028   1  19   4.75  .042 *   0.200 [0.004, 0.420]
## Condition:Visi            0.000 0.019   1  19   0.00  .980     0.000 [0.000, 1.000]
## Face:Visi                 0.105 0.050   1  19   2.10  .164     0.099 [0.000, 0.316]
## Odor:Visi                 0.022 0.035   1  19   0.64  .434     0.032 [0.000, 0.221]
## Condition:Face:Odor       0.106 0.014   1  19   7.34  .014 *   0.279 [0.035, 0.488]
## Condition:Face:Visi       5.550 0.042   1  19 133.36 <.001 *** 0.875 [0.753, 0.914]
## Condition:Odor:Visi       0.211 0.020   1  19  10.38  .004 **  0.353 [0.078, 0.547]
## Face:Odor:Visi            0.056 0.047   1  19   1.21  .285     0.060 [0.000, 0.266]
## Condition:Face:Odor:Visi  0.011 0.014   1  19   0.78  .389     0.039 [0.000, 0.233]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                               ω²      η²   η²[G]   η²[p] Cohen's f
## Condition                  0.000   0.000   0.000   0.002     0.045
## Face                       0.000   0.000   0.001   0.012     0.110
## Odor                       0.015   0.016   0.028   0.247     0.573
## Visi                       0.012   0.013   0.022   0.287     0.634
## Condition:Face             0.198   0.199   0.265   0.919     3.368
## Condition:Odor             0.000   0.001   0.002   0.038     0.199
## Face:Odor                  0.004   0.005   0.008   0.200     0.500
## Condition:Visi             0.000   0.000   0.000   0.000     0.000
## Face:Visi                  0.003   0.004   0.007   0.099     0.331
## Odor:Visi                  0.000   0.001   0.001   0.032     0.182
## Condition:Face:Odor        0.003   0.004   0.007   0.279     0.622
## Condition:Face:Visi        0.195   0.195   0.261   0.875     2.646
## Condition:Odor:Visi        0.007   0.007   0.013   0.353     0.739
## Face:Odor:Visi             0.001   0.002   0.004   0.060     0.253
## Condition:Face:Odor:Visi   0.000   0.000   0.001   0.039     0.201
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 绘图

## 
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ─────────────────────────────────
##  Condition Face Odor Mean S.D.  N
## ─────────────────────────────────
##         PU    F    P 0.32 0.17 20
##         PU    F    U 0.22 0.17 20
##         PU    H    P 0.34 0.19 20
##         PU    H    U 0.22 0.17 20
##         UP    F    P 0.26 0.28 20
##         UP    F    U 0.32 0.17 20
##         UP    H    P 0.36 0.34 20
##         UP    H    U 0.40 0.44 20
## ─────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_PU_Face_F_Odor_P, condition_PU_Face_F_Odor_U, condition_PU_Face_H_Odor_P, condition_PU_Face_H_Odor_U, condition_UP_Face_F_Odor_P, condition_UP_Face_F_Odor_U, condition_UP_Face_H_Odor_P, condition_UP_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor
## Covariate(s):               -
## ─────────────────────────────────────────────────────────────────────────────
##                         MS   MSE df1 df2     F     p      η²p   [90%     CI]
## ─────────────────────────────────────────────────────────────────────────────
## Condition            0.144 0.119   1  19  1.21  .285     0.060 [0.000, 0.266]
## Face                 0.095 0.061   1  19  1.57  .226     0.076 [0.000, 0.288]
## Odor                 0.036 0.018   1  19  2.06  .167     0.098 [0.000, 0.315]
## Condition:Face       0.071 0.053   1  19  1.34  .262     0.066 [0.000, 0.274]
## Condition:Odor       0.253 0.024   1  19 10.69  .004 **  0.360 [0.082, 0.552]
## Face:Odor            0.004 0.021   1  19  0.21  .649     0.011 [0.000, 0.166]
## Condition:Face:Odor  0.000 0.033   1  19  0.00  .964     0.000 [0.000, 1.000]
## ─────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                          ω²      η²   η²[G]   η²[p] Cohen's f
## Condition             0.010   0.013   0.014   0.060     0.253
## Face                  0.006   0.009   0.009   0.076     0.287
## Odor                  0.000   0.003   0.004   0.098     0.330
## Condition:Face        0.004   0.007   0.007   0.066     0.266
## Condition:Odor        0.020   0.023   0.024   0.360     0.750
## Face:Odor            -0.003   0.000   0.000   0.011     0.105
## Condition:Face:Odor  -0.003   0.000   0.000   0.000     0.000
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 全部8个条件
## [1] "AmyOdor"
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ───────────────────────────────────────
##  Condition Face Odor Visi  Mean S.D.  N
## ───────────────────────────────────────
##         PU    F    P    I -0.04 0.21 20
##         PU    F    P    V  0.28 0.17 20
##         PU    F    U    I -0.09 0.13 20
##         PU    F    U    V -0.28 0.15 20
##         PU    H    P    I -0.08 0.15 20
##         PU    H    P    V  0.26 0.19 20
##         PU    H    U    I -0.05 0.18 20
##         PU    H    U    V -0.23 0.18 20
##         UP    F    P    I  0.06 0.27 20
##         UP    F    P    V -0.18 0.16 20
##         UP    F    U    I -0.11 0.16 20
##         UP    F    U    V  0.20 0.12 20
##         UP    H    P    I  0.02 0.29 20
##         UP    H    P    V -0.33 0.15 20
##         UP    H    U    I -0.10 0.28 20
##         UP    H    U    V  0.29 0.23 20
## ───────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_PU_Face_F_Odor_P_Visi_I, condition_PU_Face_F_Odor_P_Visi_V, condition_PU_Face_F_Odor_U_Visi_I, condition_PU_Face_F_Odor_U_Visi_V, condition_PU_Face_H_Odor_P_Visi_I, condition_PU_Face_H_Odor_P_Visi_V, condition_PU_Face_H_Odor_U_Visi_I, condition_PU_Face_H_Odor_U_Visi_V, condition_UP_Face_F_Odor_P_Visi_I, condition_UP_Face_F_Odor_P_Visi_V, condition_UP_Face_F_Odor_U_Visi_I, condition_UP_Face_F_Odor_U_Visi_V, condition_UP_Face_H_Odor_P_Visi_I, condition_UP_Face_H_Odor_P_Visi_V, condition_UP_Face_H_Odor_U_Visi_I, condition_UP_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor, Visi
## Covariate(s):               -
## ───────────────────────────────────────────────────────────────────────────────────
##                              MS   MSE df1 df2      F     p      η²p   [90%     CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition                 0.008 0.034   1  19   0.22  .641     0.012 [0.000, 0.168]
## Face                      0.004 0.032   1  19   0.14  .712     0.007 [0.000, 0.149]
## Odor                      0.178 0.036   1  19   4.89  .039 *   0.205 [0.006, 0.424]
## Visi                      0.210 0.017   1  19  12.52  .002 **  0.397 [0.109, 0.581]
## Condition:Face            0.015 0.009   1  19   1.66  .213     0.081 [0.000, 0.293]
## Condition:Odor            4.012 0.031   1  19 130.51 <.001 *** 0.873 [0.748, 0.912]
## Face:Odor                 0.226 0.024   1  19   9.51  .006 **  0.334 [0.065, 0.532]
## Condition:Visi            0.045 0.008   1  19   5.35  .032 *   0.220 [0.011, 0.437]
## Face:Visi                 0.000 0.021   1  19   0.01  .936     0.000 [0.000, 0.024]
## Odor:Visi                 0.101 0.062   1  19   1.63  .217     0.079 [0.000, 0.291]
## Condition:Face:Odor       0.028 0.010   1  19   2.83  .109     0.130 [0.000, 0.350]
## Condition:Face:Visi       0.005 0.012   1  19   0.37  .552     0.019 [0.000, 0.191]
## Condition:Odor:Visi       6.838 0.110   1  19  62.25 <.001 *** 0.766 [0.559, 0.840]
## Face:Odor:Visi            0.042 0.033   1  19   1.26  .275     0.062 [0.000, 0.269]
## Condition:Face:Odor:Visi  0.053 0.032   1  19   1.67  .212     0.081 [0.000, 0.294]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                               ω²      η²   η²[G]   η²[p] Cohen's f
## Condition                 -0.001   0.000   0.001   0.012     0.110
## Face                      -0.001   0.000   0.000   0.007     0.084
## Odor                       0.006   0.008   0.015   0.205     0.508
## Visi                       0.008   0.009   0.018   0.397     0.811
## Condition:Face            -0.001   0.001   0.001   0.081     0.297
## Condition:Odor             0.170   0.172   0.258   0.873     2.622
## Face:Odor                  0.008   0.010   0.019   0.334     0.708
## Condition:Visi             0.001   0.002   0.004   0.220     0.531
## Face:Visi                 -0.001   0.000   0.000   0.000     0.000
## Odor:Visi                  0.003   0.004   0.009   0.079     0.293
## Condition:Face:Odor        0.000   0.001   0.002   0.130     0.387
## Condition:Face:Visi       -0.001   0.000   0.000   0.019     0.139
## Condition:Odor:Visi        0.291   0.293   0.372   0.766     1.809
## Face:Odor:Visi             0.000   0.002   0.004   0.062     0.257
## Condition:Face:Odor:Visi   0.001   0.002   0.005   0.081     0.297
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 绘图

## 
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ─────────────────────────────────
##  Condition Face Odor Mean S.D.  N
## ─────────────────────────────────
##         PU    F    P 0.31 0.17 20
##         PU    F    U 0.19 0.13 20
##         PU    H    P 0.27 0.14 20
##         PU    H    U 0.16 0.13 20
##         UP    F    P 0.21 0.15 20
##         UP    F    U 0.26 0.17 20
##         UP    H    P 0.29 0.14 20
##         UP    H    U 0.23 0.11 20
## ─────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_PU_Face_F_Odor_P, condition_PU_Face_F_Odor_U, condition_PU_Face_H_Odor_P, condition_PU_Face_H_Odor_U, condition_UP_Face_F_Odor_P, condition_UP_Face_F_Odor_U, condition_UP_Face_H_Odor_P, condition_UP_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor
## Covariate(s):               -
## ────────────────────────────────────────────────────────────────────────────
##                         MS   MSE df1 df2    F     p      η²p   [90%     CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition            0.011 0.037   1  19 0.29  .595     0.015 [0.000, 0.180]
## Face                 0.001 0.022   1  19 0.04  .835     0.002 [0.000, 0.104]
## Odor                 0.140 0.015   1  19 9.47  .006 **  0.333 [0.065, 0.531]
## Condition:Face       0.036 0.026   1  19 1.41  .250     0.069 [0.000, 0.278]
## Condition:Odor       0.117 0.015   1  19 7.93  .011 *   0.294 [0.043, 0.501]
## Face:Odor            0.019 0.007   1  19 2.65  .120     0.122 [0.000, 0.342]
## Condition:Face:Odor  0.034 0.018   1  19 1.86  .188     0.089 [0.000, 0.304]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                          ω²      η²   η²[G]   η²[p] Cohen's f
## Condition            -0.002   0.003   0.003   0.015     0.123
## Face                 -0.005   0.000   0.000   0.002     0.045
## Odor                  0.034   0.039   0.042   0.333     0.707
## Condition:Face        0.005   0.010   0.011   0.069     0.272
## Condition:Odor        0.028   0.033   0.035   0.294     0.645
## Face:Odor             0.000   0.005   0.006   0.122     0.373
## Condition:Face:Odor   0.004   0.009   0.010   0.089     0.313
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 全部8个条件
## [1] "lateralAmyOdor"
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ───────────────────────────────────────
##  Condition Face Odor Visi  Mean S.D.  N
## ───────────────────────────────────────
##         PU    F    P    I -0.09 0.13 20
##         PU    F    P    V  0.22 0.12 20
##         PU    F    U    I -0.07 0.10 20
##         PU    F    U    V -0.22 0.13 20
##         PU    H    P    I -0.06 0.12 20
##         PU    H    P    V  0.19 0.16 20
##         PU    H    U    I -0.06 0.15 20
##         PU    H    U    V -0.17 0.14 20
##         UP    F    P    I  0.00 0.24 20
##         UP    F    P    V -0.15 0.16 20
##         UP    F    U    I -0.11 0.17 20
##         UP    F    U    V  0.14 0.13 20
##         UP    H    P    I -0.07 0.22 20
##         UP    H    P    V -0.30 0.13 20
##         UP    H    U    I -0.04 0.17 20
##         UP    H    U    V  0.18 0.12 20
## ───────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_PU_Face_F_Odor_P_Visi_I, condition_PU_Face_F_Odor_P_Visi_V, condition_PU_Face_F_Odor_U_Visi_I, condition_PU_Face_F_Odor_U_Visi_V, condition_PU_Face_H_Odor_P_Visi_I, condition_PU_Face_H_Odor_P_Visi_V, condition_PU_Face_H_Odor_U_Visi_I, condition_PU_Face_H_Odor_U_Visi_V, condition_UP_Face_F_Odor_P_Visi_I, condition_UP_Face_F_Odor_P_Visi_V, condition_UP_Face_F_Odor_U_Visi_I, condition_UP_Face_F_Odor_U_Visi_V, condition_UP_Face_H_Odor_P_Visi_I, condition_UP_Face_H_Odor_P_Visi_V, condition_UP_Face_H_Odor_U_Visi_I, condition_UP_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor, Visi
## Covariate(s):               -
## ───────────────────────────────────────────────────────────────────────────────────
##                              MS   MSE df1 df2      F     p      η²p   [90%     CI]
## ───────────────────────────────────────────────────────────────────────────────────
## Condition                 0.012 0.044   1  19   0.27  .608     0.014 [0.000, 0.177]
## Face                      0.005 0.012   1  19   0.41  .532     0.021 [0.000, 0.196]
## Odor                      0.009 0.018   1  19   0.49  .494     0.025 [0.000, 0.205]
## Visi                      0.181 0.018   1  19  10.16  .005 **  0.348 [0.075, 0.543]
## Condition:Face            0.040 0.006   1  19   6.41  .020 *   0.252 [0.023, 0.466]
## Condition:Odor            2.705 0.013   1  19 207.62 <.001 *** 0.916 [0.831, 0.942]
## Face:Odor                 0.217 0.020   1  19  11.03  .004 **  0.367 [0.087, 0.558]
## Condition:Visi            0.057 0.015   1  19   3.74  .068 .   0.165 [0.000, 0.386]
## Face:Visi                 0.017 0.017   1  19   1.00  .331     0.050 [0.000, 0.251]
## Odor:Visi                 0.001 0.023   1  19   0.06  .810     0.003 [0.000, 0.115]
## Condition:Face:Odor       0.084 0.013   1  19   6.54  .019 *   0.256 [0.025, 0.469]
## Condition:Face:Visi       0.008 0.005   1  19   1.66  .213     0.080 [0.000, 0.293]
## Condition:Odor:Visi       3.491 0.018   1  19 191.67 <.001 *** 0.910 [0.819, 0.938]
## Face:Odor:Visi            0.027 0.020   1  19   1.37  .256     0.067 [0.000, 0.276]
## Condition:Face:Odor:Visi  0.002 0.017   1  19   0.13  .722     0.007 [0.000, 0.146]
## ───────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                               ω²      η²   η²[G]   η²[p] Cohen's f
## Condition                  0.000   0.001   0.002   0.014     0.119
## Face                      -0.001   0.000   0.001   0.021     0.146
## Odor                      -0.001   0.001   0.001   0.025     0.160
## Visi                       0.012   0.013   0.025   0.348     0.731
## Condition:Face             0.002   0.003   0.006   0.252     0.580
## Condition:Odor             0.191   0.193   0.274   0.916     3.302
## Face:Odor                  0.014   0.015   0.029   0.367     0.761
## Condition:Visi             0.003   0.004   0.008   0.165     0.445
## Face:Visi                  0.000   0.001   0.002   0.050     0.229
## Odor:Visi                 -0.001   0.000   0.000   0.003     0.055
## Condition:Face:Odor        0.005   0.006   0.012   0.256     0.587
## Condition:Face:Visi       -0.001   0.001   0.001   0.080     0.295
## Condition:Odor:Visi        0.247   0.249   0.327   0.910     3.180
## Face:Odor:Visi             0.001   0.002   0.004   0.067     0.268
## Condition:Face:Odor:Visi  -0.001   0.000   0.000   0.007     0.084
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 绘图

## 
## 作差并取绝对值之后
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ─────────────────────────────────
##  Condition Face Odor Mean S.D.  N
## ─────────────────────────────────
##         PU    F    P 0.32 0.19 20
##         PU    F    U 0.26 0.19 20
##         PU    H    P 0.38 0.22 20
##         PU    H    U 0.25 0.21 20
##         UP    F    P 0.30 0.39 20
##         UP    F    U 0.38 0.23 20
##         UP    H    P 0.44 0.45 20
##         UP    H    U 0.49 0.72 20
## ─────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_PU_Face_F_Odor_P, condition_PU_Face_F_Odor_U, condition_PU_Face_H_Odor_P, condition_PU_Face_H_Odor_U, condition_UP_Face_F_Odor_P, condition_UP_Face_F_Odor_U, condition_UP_Face_H_Odor_P, condition_UP_Face_H_Odor_U
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor
## Covariate(s):               -
## ────────────────────────────────────────────────────────────────────────────
##                         MS   MSE df1 df2    F     p      η²p   [90%     CI]
## ────────────────────────────────────────────────────────────────────────────
## Condition            0.406 0.261   1  19 1.55  .228     0.076 [0.000, 0.287]
## Face                 0.221 0.094   1  19 2.34  .142     0.110 [0.000, 0.329]
## Odor                 0.009 0.028   1  19 0.33  .575     0.017 [0.000, 0.185]
## Condition:Face       0.090 0.112   1  19 0.80  .381     0.041 [0.000, 0.236]
## Condition:Odor       0.252 0.044   1  19 5.74  .027 *   0.232 [0.015, 0.448]
## Face:Odor            0.028 0.061   1  19 0.46  .504     0.024 [0.000, 0.203]
## Condition:Face:Odor  0.003 0.065   1  19 0.04  .842     0.002 [0.000, 0.100]
## ────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                          ω²      η²   η²[G]   η²[p] Cohen's f
## Condition             0.016   0.019   0.019   0.076     0.287
## Face                  0.007   0.010   0.011   0.110     0.352
## Odor                 -0.003   0.000   0.000   0.017     0.132
## Condition:Face        0.001   0.004   0.004   0.041     0.207
## Condition:Odor        0.009   0.012   0.012   0.232     0.550
## Face:Odor            -0.002   0.001   0.001   0.024     0.157
## Condition:Face:Odor  -0.003   0.000   0.000   0.002     0.045
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 全部8个条件
## [1] "medialAmyOdor"
## ====== MANOVA Output (Within-Subjects Design) ======
## 
## Descriptive Statistics:
## ───────────────────────────────────────
##  Condition Face Odor Visi  Mean S.D.  N
## ───────────────────────────────────────
##         PU    F    P    I -0.01 0.27 20
##         PU    F    P    V  0.31 0.20 20
##         PU    F    U    I -0.09 0.16 20
##         PU    F    U    V -0.30 0.18 20
##         PU    H    P    I -0.08 0.17 20
##         PU    H    P    V  0.30 0.20 20
##         PU    H    U    I -0.05 0.22 20
##         PU    H    U    V -0.26 0.20 20
##         UP    F    P    I  0.10 0.37 20
##         UP    F    P    V -0.18 0.19 20
##         UP    F    U    I -0.12 0.20 20
##         UP    F    U    V  0.26 0.14 20
##         UP    H    P    I  0.07 0.38 20
##         UP    H    P    V -0.36 0.18 20
##         UP    H    U    I -0.14 0.43 20
##         UP    H    U    V  0.34 0.35 20
## ───────────────────────────────────────
## Total sample size: N = 20
## 
## ANOVA Table:
## Dependent variable(s):      condition_PU_Face_F_Odor_P_Visi_I, condition_PU_Face_F_Odor_P_Visi_V, condition_PU_Face_F_Odor_U_Visi_I, condition_PU_Face_F_Odor_U_Visi_V, condition_PU_Face_H_Odor_P_Visi_I, condition_PU_Face_H_Odor_P_Visi_V, condition_PU_Face_H_Odor_U_Visi_I, condition_PU_Face_H_Odor_U_Visi_V, condition_UP_Face_F_Odor_P_Visi_I, condition_UP_Face_F_Odor_P_Visi_V, condition_UP_Face_F_Odor_U_Visi_I, condition_UP_Face_F_Odor_U_Visi_V, condition_UP_Face_H_Odor_P_Visi_I, condition_UP_Face_H_Odor_P_Visi_V, condition_UP_Face_H_Odor_U_Visi_I, condition_UP_Face_H_Odor_U_Visi_V
## Between-subjects factor(s): -
## Within-subjects factor(s):  Condition, Face, Odor, Visi
## Covariate(s):               -
## ──────────────────────────────────────────────────────────────────────────────────
##                              MS   MSE df1 df2     F     p      η²p   [90%     CI]
## ──────────────────────────────────────────────────────────────────────────────────
## Condition                 0.029 0.051   1  19  0.57  .460     0.029 [0.000, 0.214]
## Face                      0.031 0.040   1  19  0.77  .390     0.039 [0.000, 0.233]
## Odor                      0.319 0.064   1  19  5.01  .037 *   0.209 [0.007, 0.427]
## Visi                      0.238 0.025   1  19  9.56  .006 **  0.335 [0.066, 0.533]
## Condition:Face            0.033 0.013   1  19  2.46  .133     0.115 [0.000, 0.334]
## Condition:Odor            4.572 0.057   1  19 80.85 <.001 *** 0.810 [0.634, 0.869]
## Face:Odor                 0.250 0.042   1  19  5.95  .025 *   0.239 [0.018, 0.454]
## Condition:Visi            0.025 0.012   1  19  2.17  .157     0.102 [0.000, 0.320]
## Face:Visi                 0.000 0.037   1  19  0.00  .958     0.000 [0.000, 1.000]
## Odor:Visi                 0.277 0.131   1  19  2.11  .162     0.100 [0.000, 0.317]
## Condition:Face:Odor       0.011 0.017   1  19  0.65  .430     0.033 [0.000, 0.222]
## Condition:Face:Visi       0.017 0.037   1  19  0.45  .509     0.023 [0.000, 0.202]
## Condition:Odor:Visi       9.068 0.216   1  19 41.98 <.001 *** 0.688 [0.438, 0.787]
## Face:Odor:Visi            0.043 0.069   1  19  0.62  .439     0.032 [0.000, 0.220]
## Condition:Face:Odor:Visi  0.132 0.053   1  19  2.48  .132     0.116 [0.000, 0.335]
## ──────────────────────────────────────────────────────────────────────────────────
## MSE = Mean Square Error (an estimate of the population variance σ²)
## 
## ANOVA Effect Size:
##                               ω²      η²   η²[G]   η²[p] Cohen's f
## Condition                 -0.001   0.001   0.001   0.029     0.173
## Face                      -0.001   0.001   0.002   0.039     0.201
## Odor                       0.008   0.009   0.016   0.209     0.514
## Visi                       0.005   0.007   0.012   0.335     0.710
## Condition:Face            -0.001   0.001   0.002   0.115     0.360
## Condition:Odor             0.130   0.132   0.188   0.810     2.065
## Face:Odor                  0.006   0.007   0.013   0.239     0.560
## Condition:Visi            -0.001   0.001   0.001   0.102     0.337
## Face:Visi                 -0.002   0.000   0.000   0.000     0.000
## Odor:Visi                  0.006   0.008   0.014   0.100     0.333
## Condition:Face:Odor       -0.001   0.000   0.001   0.033     0.185
## Condition:Face:Visi       -0.001   0.000   0.001   0.023     0.153
## Condition:Odor:Visi        0.259   0.261   0.315   0.688     1.485
## Face:Odor:Visi             0.000   0.001   0.002   0.032     0.182
## Condition:Face:Odor:Visi   0.002   0.004   0.007   0.116     0.362
## 
## ω² = omega-squared = (SS - df1 * MSE) / (SST + MSE)
## η² = eta-squared = SS / SST
## η²G = generalized eta-squared (see Olejnik & Algina, 2003)
## η²p = partial eta-squared = SS / (SS + SSE) = F * df1 / (F * df1 + df2)
## Cohen’s f = sqrt( η²p / (1 - η²p) )
## 
## Levene’s Test for Homogeneity of Variance:
## 
## Mauchly’s Test of Sphericity:
## 
## 
## 绘图

# # 可以查看对应主题的颜色
# colour_plot(swatch())
# colour_plot(ggthemr("pale"))
# colour_plot(ggthemr("greyscale"))
# colour_plot(ggthemr("solarized"))
# # colorbrewer选择颜色,生成颜色
# display.brewer.all_block()
# colors <- brewer.pal(8,"Set2")
# colour_plot(colors)
# # colors是自带颜色函数
# colors()